4KAgent: Agentic Any Image to 4K Super-Resolution

Gengchen Mai; James Zou; Jian Wang; Lihong V. Wang; Ming-Hsuan Yang; Mingyang Wu; Qi Zheng; Renjie Li; Xiaoyu Wang; Xinrui Jiang

REVIEW 4 major objections 5 minor 1 cited by

4KAgent: Agentic Any Image to 4K Super-Resolution

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An agentic system claims any image can be upscaled to 4K without retraining.

desk verdict A serious engineering effort whose SOTA claim is undercut by selecting on the same no-reference metrics used for evaluation; worth reviewing, but needs independent metrics. read the letter →

arxiv 2507.07105 v1 pith:GUUEXHJX submitted 2025-07-09 cs.CV eess.IV

Yushen Zuo , Qi Zheng , Mingyang Wu , Xinrui Jiang , Renjie Li , Jian Wang , Yide Zhang , Gengchen Mai

show 5 more authors

Lihong V. Wang James Zou Xiaoyu Wang Ming-Hsuan Yang Zhengzhong Tu

This is my paper · ORCID

classification cs.CVeess.IV

keywords agenticsuper-resolution4Kupscalingquality-drivenmixture-of-expertsimagerestorationno-referencequalityassessmentfacescientificimagingzero-shotgeneralization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

4KAgent claims that a single agentic system, without retraining or domain adaptation, can upscale any image to 4K resolution—including degraded 256×256 inputs that need 16× magnification—by having a vision-language Perception Agent diagnose degradations and plan a restoration sequence, and a Restoration Agent run every relevant specialist model in a toolbox at each step, keeping the output that scores best under a quality-driven mixture-of-experts policy. If correct, this replaces the usual practice of training or selecting a separate expert model for each domain with one configurable generalist that works across natural photos, faces, AI-generated images, satellite imagery, microscopy, pathology, and medical imaging. The paper reports new state-of-the-art perceptual-quality results on most of the 26 benchmarks spanning 11 task categories, while remaining competitive on fidelity metrics like PSNR where the profile targets fidelity. The central reason this matters is that agentic planning plus expert selection is claimed to be a viable route to universal super-resolution, rather than yet another architecture trained for one degradation.

What carries the argument

The load-bearing mechanism is the Q-MoE (quality-driven mixture-of-experts) selection policy in Eqs. (2)–(4). At each restoration step, every expert in the toolbox processes the current image; each candidate is scored by $Q_s = H(T_i(I_{k-1}), C_I) + Q_{nr}/4$, where $Q_{nr} = w_{NIQE}\cdot(1 - Q_{NIQE}/10) + w_{MUSIQ}\cdot Q_{MUSIQ} + w_{MANIQA}\cdot Q_{MANIQA} + w_{CLIPIQA}\cdot Q_{CLIPIQA}$ with weights $(1.0, 0.01, 1.0, 1.0)$, and the argmax is kept. This converts a fixed toolbox of published specialists into a single adaptive system. The automatic scale rule $s = \min\{s \in \{2,4,8,16\} \mid \max(H_I,W_I)\cdot s \ge 4000\} \cup \{16\}$ is what lets the same system claim 'any image to 4K'; when $s=16$ the agenda appends two $4\times$ super-resolution steps, making the upscaling recursive. The face pipeline extends the same selection idea with a face-specific score $Q_f^s$ combining ArcFace identity cosine and CLIB-FIQA.

What would settle it

Re-run the DIV4K-50, RealSRSet, and MiO Group C comparisons with a fixed plan (denoise, deblur, 4× SR, 4× SR) while choosing outputs by each single metric alone, for example argmax MUSIQ; if the fixed plan plus single-metric selection matches or beats 4KAgent's reported scores, the agentic planning and Q-MoE add nothing. Complement this with a human A/B test on upscaled 4K crops: if raters prefer DiffBIR-16× or OSEDiff outputs over Q-MoE selections, the metric-driven central claim fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that image super-resolution can be reframed as an agentic loop—analyze, plan, execute, reflect, roll back—instead of a single learned mapping from low to high resolution. The Perception Agent combines four image-quality scores (CLIPIQA, TOPIQ, MUSIQ, NIQE) with a vision-language model to output a degradation list, an image description, and a restoration agenda; a planner then orders restoration tasks, with the scale factor chosen automatically so that the long side reaches 4000 pixels. The Restoration Agent executes each task by applying all toolbox experts and selecting the output with the highest Q-MoE score, defined as the human-preference score HPSv2 plus a weighted sum of inverted NIQE, MUSIQ, MANIQA, and CLIPIQA; a rollback triggers re-planning when the score drops below a threshold. A separate face-restoration stage crops detected faces, applies face experts, and selects by a face-level score that combines ArcFace identity similarity and CLIB-FIQA. The paper claims this pipeline achieves state-of-the-art perceptual quality on 26 benchmarks and makes 16× 4K upscaling practical without any training.

Load-bearing premise

The load-bearing premise is that the no-reference perceptual metrics used both to select outputs and to rank methods (NIQE, MUSIQ, MANIQA, CLIPIQA) are reliable proxies for human judgment of image quality at 4K resolution.

Editorial extensions

If this is right

A single, training-free system could replace per-domain SR specialists: the same toolbox and Q-MoE selection top the perceptual metrics for natural photos, faces, AIGC, satellite, microscopy, pathology, X-ray, ultrasound, and fundoscopy benchmarks.
Degraded 256×256 images can be restored and magnified to 4096×4096 in one pipeline; because the scale rule is recursive, the same mechanism extends to arbitrarily large outputs.
Users can switch between a perceptual and a fidelity-oriented system by choosing a profile—the same weights and toolbox, different expert subsets—which is exactly the flexibility the paper claims specialized models lack.
For AI-generated content, upscaling 1K generations with 4KAgent reports better NIQE, CLIPIQA, and PickScore than native 4K generation from SANA-4K or Diffusion-4K, pointing to cheaper 4K production.
The perception–distortion tradeoff remains visible: perception profiles gain on no-reference metrics while fidelity profiles stay near the top in PSNR and SSIM, supporting the paper's claim that profile choice controls the tradeoff.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Since the Q-MoE selection score is built from the same MUSIQ, MANIQA, CLIPIQA, and NIQE metrics used in the paper's leader tables, part of the reported margin may be the evaluator serving as its own optimizer; a human-rating study would be needed to confirm the perceptual gains are real rather than metric-aligned.
The 'any image' claim holds only within the fixed toolbox: generalization is selection among a strong fixed menu, so adding new experts (for example, modality-specific medical or scientific models) would directly extend the system, and the paper's modular design invites this.
The hand-set weights in $Q_s$ and $Q_f^s$ are not validated against held-out human judgments; a sensitivity sweep could show how much of the state-of-the-art margin depends on those specific weights.
A natural untested extension is video: per-frame Q-MoE selection is embarrassingly parallel, and the paper's streaming applications suggest 4KAgent could upscale video in real time if inference latency is cut.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: 4KAgent is a real system with genuine modularity, but the central SOTA claim is not load-bearing because the selection policy optimizes the exact no-reference metrics used for evaluation. I would send this to review, not desk reject, but a revision must add independent or human-judgment metrics.

What is actually new: the combination at 4K scale — Q-MoE selection, the face restoration pipeline, the profile module, and the DIV4K-50 benchmark. The evaluation spans 26 benchmarks across natural, AIGC, remote sensing, microscopy, pathology, and medical images. That breadth is real, and the ablation shows Q-MoE helps over the DFS baseline. The profile module is a practical idea, and the authors plan to release code and models, which is useful.

The soft spot is hard to miss. Eqs. (2)-(4) define the selection score as a weighted sum of NIQE, MUSIQ, MANIQA, and CLIPIQA with hand-picked weights, and Tables 3-22 report overwhelmingly those same metrics. The system returns the arg max over toolbox outputs, so winning those metrics is structurally expected. The paper itself concedes in Sec. 6.1 that no-reference metrics may become misaligned with human judgment at 4K, yet relies on them for the headline claims. On RealSR and DrealSR, 4KAgent loses to PiSA-SR on LPIPS and DISTS, metrics not in the selection score. So the “state-of-the-art perceptual quality” claim holds only for the metrics being optimized. There are no error bars or significance tests anywhere.

The “any image to 4K” claim is also broader than the evidence: the joint restoration + 4K benchmark is 50 images from one dataset, and the 16x experiments use 20 images. The medical results look suspiciously clean — beating a specialized ultrasound model by 3 dB PSNR without training on that domain is possible only if the toolbox happens to contain a good fit, which weakens the generalist framing.

Who this is for: anyone working on agentic image restoration or benchmark design. It is a useful system paper and a cautionary example of selection-evaluation circularity. My recommendation: send it to peer review, but require the revision to report at least one reference-based or human-judgment metric on the main benchmarks, and to ablate the Q-MoE weights. As submitted, I would not accept.

Referee Report

4 major / 5 minor

Summary. The paper presents 4KAgent, a multi-agent framework for universal image super-resolution that combines a vision-language perception agent, a toolbox of restoration models, a quality-driven mixture-of-experts selection policy (Q-MoE), a face-restoration pipeline, and a user-configurable profile module. The system is evaluated on 26 benchmarks spanning classical SR, real-world SR, multiple-degradation restoration, face restoration, 16x upscaling, 4K joint restoration, AI-generated content, remote sensing, fluorescence microscopy, pathology, X-ray, ultrasound, and fundoscopy. The central claim is that 4KAgent sets a new state of the art across most of these benchmarks in terms of perceptual quality and, in some settings, fidelity, while requiring no task-specific retraining.

Significance. If the central claim were established, 4KAgent would be a significant contribution: a training-free generalist that outperforms specialized models across many imaging domains would advance both agentic low-level vision and practical deployment of super-resolution. The paper's strengths include the breadth of the evaluation, the modular and interpretable design, the introduction of a new 4K benchmark (DIV4K-50), and the commitment to release code and models. However, the headline SOTA claim is currently supported mainly by no-reference metrics that are the same ones used for output selection, so the independent evidence for a genuine perceptual advantage is missing. As presented, the significance of the results is therefore not yet established.

major comments (4)

[Sec. 2.3, Eqs. (2)-(4), and Tabs. 3-22] The Q-MoE selection score Qs is defined as a weighted sum of HPSv2, NIQE, MUSIQ, MANIQA, and CLIPIQA (Eqs. 2-3), and the final output is chosen as arg max Qs (Eq. 4). These exact no-reference metrics—NIQE, MUSIQ, MANIQA, CLIPIQA—are also the primary evaluation metrics in the benchmark tables (e.g., Tabs. 3-9, 12-17). Consequently, the reported gains on those metrics are partly forced by construction: the system selects the output that maximizes the evaluation criterion itself. The paper needs to provide evaluation on metrics that are not part of Qs, or a human perceptual study, to support the claim of improved perceptual quality. The currently reported reference-based perceptual metrics do not support the claim: on RealSR and DrealSR (Tab. 5), 4KAgent (ExpSR-s4-P) is worse than PiSA-SR on LPIPS, DISTS, and FID (e.g., RealSR LPIPS 0.3253 vs. 0.2672; DISTS 0.2292 vs. 0.2044), and in multiple-degradation restoration (Tab. 6) 4KAgent trails MAIR on SSIM and LPIPS. Without additional independent metrics, the SOTA claim on perceptual quality is not established.
[Sec. 6.1 and Tabs. 8-9] The paper itself states in Sec. 6.1 that 'as image resolution approaches 4K and beyond, existing no-reference perceptual metrics may become misaligned with human judgment of visual quality,' yet the 16x upscaling results (Tab. 8) and the joint restoration + 4K upscaling results (Tab. 9) are evaluated exclusively with NIQE, CLIPIQA, MUSIQ, and MANIQA—the very metrics that Q-MoE optimizes. This is a self-acknowledged limitation that directly undermines the central 4K and 16x claims. The authors should either provide human evaluation on these settings or use reference-based perceptual metrics where a ground truth exists (e.g., DIV4K-50 has HQ references), and should not report SOTA status based solely on the selection metrics.
[General experimental protocol (e.g., Sec. 5.1, Sec. 6.1)] No error bars, confidence intervals, or significance tests are reported for any of the 26 benchmarks. The system involves stochastic components (LLM/VLM planning, diffusion-based tools with random sampling), and the selection process itself may introduce variability. For a fair comparison with feedforward baselines, especially when claiming SOTA margins, the paper should report mean and standard deviation over multiple independent runs, or at least provide a statistical comparison on a subset of benchmarks. Without these, the numerical differences in the tables cannot be assessed for reliability.
[Sec. 5.1, Tabs. 3-4] The claim in the introduction and Sec. 5.1 that 4KAgent 'sets new state-of-the-art on perceptual quality' is not supported by the reference-based perceptual metrics that are reported in the same tables. On the classical SR benchmarks, 4KAgent (ExpSR-s4-P) does not achieve the best LPIPS or DISTS on any of the five datasets; for example, on Set5 its LPIPS is 0.1591 vs. 0.1318 for PiSA-SR, and on Manga109 it is 0.1776 vs. 0.1450. The SOTA claim is therefore limited to the no-reference metrics that the system explicitly optimizes. The paper should temper the general language or provide evidence from reference-based metrics that the perceptual gain is real.

minor comments (5)

[Abstract and Sec. 2.2] The abstract claims the system can upscale 'any image' but Sec. 2.2 states that 4KAgent handles images 'resolution larger than 250x250'; please clarify the scope in the abstract.
[Sec. 2.2] There are typographical errors in this section: 'incroporating' should be 'incorporating' and 'coporating' should be 'incorporating'.
[Sec. 7.1] The MUSIQ-P patch-based variant is introduced for AIGC 4K evaluation, but it is not used in the earlier 4K-related experiments (e.g., Sec. 6). Consider defining it once in a general evaluation section and applying it consistently to all ultra-high-resolution evaluations.
[Sec. 8.2, Tab. 18] The row for 4KAgent (ExpSR-sN-F) reports results for x2, x4, and x8 in a single row, which is visually confusing. Please split these into separate rows or clearly mark that the same row spans three scale factors.
[Sec. 3.4] The hyperparameters w_NIQE=1.0, w_MUSIQ=0.01, w_MANIQA=1.0, w_CLIPIQA=1.0, w_IP=0.001, w_IQA=1.0, and eta=0.5 appear hand-selected without sensitivity analysis. A small study showing robustness to these choices would strengthen the Q-MoE design.

Circularity Check

2 steps flagged · score 6.0 of 10

Partial circularity: Q-MoE optimizes the same no-reference metrics used to claim SOTA, so the perceptual gains are partly selection artifacts.

fitted input called prediction [Sec. 2.3 Eqs. (2)-(4); Sec. 3.4; Sec. 5.1]
"Qs(Ti(Ik−1)) = H(Ti(Ik−1, CI )) + Qnr(Ti(Ik−1))/4, (2) ... Qnr(Ti(Ik−1)) = wNIQE ∗ (1 − QNIQE/10) + X j∈Ω wj ∗ Qj. (3) Ω = {MUSIQ, MANIQA, CLIPIQA}. ... Ik = arg max Qs (T1(Ik−1), T2(Ik−1), ..., TN (Ik−1)). (4). Sec. 3.4: 'we set wNIQE = 1.0, wMUSIQ = 0.01, wMANIQA = 1.0, wCLIPIQA = 1.0 for Qs'. Sec. 5.1: 'NIQE, CLIPIQA, MUSIQ, and MANIQA-pipal are used to evaluate the perceptual quality of images without reference images.'"

The restoration output at every step is selected as the arg max of a weighted sum that includes exactly NIQE, MUSIQ, MANIQA, and CLIPIQA (plus HPSv2). The evaluation then reports the same four no-reference metrics in Tables 3-22 as evidence of state-of-the-art perceptual quality. Because the per-image output is chosen to maximize Qs, the reported scores on those metrics are, in large part, the selection objective evaluated back on the test images rather than an independent prediction of perceptual superiority. The hand-picked weights are free parameters, and the claim 'sets new state-of-the-art on perceptual quality' therefore reduces substantially to the system being defined to optimize the evaluation metrics.
fitted input called prediction [Sec. 2.4 Eq. (5); Sec. 5.4]
"Qf s (T f i (F l Ik )) = wIP ∗ IP(T f i (F l Ik ), Fl I ) + wIQA ∗ (Qnr(T f i (F l Ik ))/4 + QCF(T f i (F l Ik ))), (5) ... CF indicates CLIB-FIQA [154]. Sec. 5.4: 'we evaluate performance with four no-reference perceptual metrics (NIQE, CLIPIQA, MUSIQ, and MANIQA-pipal) and two advanced face-specific IQA metrics (CLIB-FIQA [154] and DSL-FIQA [27]).'"

The face-restoration pipeline selects the best face among candidates by maximizing Qf_s, which includes CLIB-FIQA (QCF) with weight wIQA=1.0. The face-restoration evaluation then reports CLIB-FIQA as one of its headline metrics and uses the improvement on it as evidence of superiority. Thus the reported CLIB-FIQA gain is partly forced by the selection rule: the chosen face is, by construction, the one scoring highest on the same metric that is later reported as a result.

full rationale

The paper's central claim of state-of-the-art perceptual quality is substantially driven by the Q-MoE selection score. Eqs. (2)-(4) select, per restoration step, the toolbox output maximizing a weighted sum of NIQE, MUSIQ, MANIQA, and CLIPIQA (plus HPSv2), with weights fixed in Sec. 3.4. The same four no-reference metrics are the headline evaluation metrics in Tables 3-22, listed in Sec. 5.1 as 'NIQE, CLIPIQA, MUSIQ, and MANIQA-pipal'. Therefore the reported gains on those metrics are partly forced by construction: they measure the selection objective rather than an independent perceptual improvement. The face-restoration variant has the same structure: Eq. (5) includes CLIB-FIQA in Qf_s, and Table 7 reports CLIB-FIQA as an evaluation metric. The paper itself concedes in Sec. 6.1 that existing no-reference perceptual metrics 'may become misaligned with human judgment of visual quality' at 4K, which further weakens the interpretation of the optimized scores as genuine perceptual gains. However, the circularity is only partial: the paper also reports metrics outside Qs (PSNR, SSIM, LPIPS, DISTS, FID, DSL-FIQA, and medical imaging metrics) and achieves leading or competitive results on several of them (e.g., PSNR in Table 6, SSIM in Table 19, PSNR/SSIM in Tables 21-22), which provide independent, though uneven, support. No load-bearing self-citation chain was found. Overall score 6.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The central claim rests on hand-chosen selection weights, the validity of no-reference IQA metrics at 4K, the sufficiency of the fixed toolbox, and the reliability of the VLM planner. No new physical entities are introduced.

free parameters (8)

w_NIQE = 1.0
Hand-chosen weight in Qs (Eq. 3) for NIQE in the Q-MoE selection; directly affects which model output is selected.
w_MUSIQ = 0.01
Hand-chosen weight in Qs (Eq. 3); despite small value it is one of the optimized evaluation metrics.
w_MANIQA = 1.0
Hand-chosen weight in Qs (Eq. 3).
w_CLIPIQA = 1.0
Hand-chosen weight in Qs (Eq. 3).
w_IP = 0.001
Hand-chosen weight for identity preservation in face Qfs (Eq. 5).
w_IQA = 1.0
Hand-chosen weight for IQA in face Qfs (Eq. 5).
rollback threshold eta = 0.5
Hand-chosen threshold in Section 3.4; controls when restoration steps are rolled back.
MUSIQ-P patch size = 512x512
Ad hoc variant of MUSIQ introduced in Section 7.1 when standard MUSIQ did not favor upscaled AIGC images.

assumptions (6)

domain assumption No-reference IQA metrics (NIQE, MUSIQ, MANIQA, CLIPIQA) are valid and reliable proxies for perceptual quality, including at 4K resolution.
Used both in Q-MoE selection (Eq. 2-3) and as evaluation metrics in all tables; the paper itself notes in Section 6.1 that these metrics may become misaligned at ultra-high resolution, yet still uses them for selection.
domain assumption The pre-trained toolbox covers all degradations present in 'any image' (noise, blur, haze, rain, JPEG, low light) and each tool works on arbitrary domains.
Supports the 'any image to 4K' claim; no training or adaptation is done, so performance depends on the fixed toolbox.
domain assumption The VLM (Llama-3.2-Vision or DepictQA) can correctly identify degradations and schedule a valid restoration plan.
The whole Perception Agent pipeline (Section 2.2) relies on this; if the plan is wrong, the Restoration Agent cannot recover.
domain assumption Restoration experience E taken from AgenticIR transfers to 4KAgent's planning.
Used in Section 2.2 and Section 3.3; no validation of transfer.
domain assumption ArcFace cosine similarity is a sufficient measure of face identity preservation.
Used in Eq. (5) for face Q-MoE; plausible but assumes pretrained face embeddings are reliable for heavily degraded faces.
domain assumption Super-resolution can be decomposed into independent restoration tasks with no interaction effects between degradations.
The sequential restoration plan assumes task order and tool independence, e.g., denoising before SR; if degradations interact, the greedy Q-MoE may be suboptimal.

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Cite this review

Pith. "Pith review of 4KAgent: Agentic Any Image to 4K Super-Resolution." pith.science (2026). https://pith.science/paper/GUUEXHJX

@misc{pith2026250707105,
  author       = {Pith},
  title        = {Pith review of: 4KAgent: Agentic Any Image to 4K Super-Resolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GUUEXHJX}},
  note         = {Machine review of arXiv:2507.07105}
}

read the original abstract

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256x256, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-expert policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We will release all the code, models, and results at: https://4kagent.github.io.

Figures

Figures reproduced from arXiv: 2507.07105 by the authors.

**Figure 1.** We present 4KAgent, an agentic image super-resolution generalist designed to universally upscale any image to 4K, regardless of input type, degradation level, or domain. That is, 4KAgent effectively restores diverse imagery, spanning from natural scenes, severely degraded captures (e.g., old photos), human/pet portraits, AI-generated content (AIGC), as well as specialized scientific imaging domains, such as remote s… view at source ↗

**Figure 2.** 4KAgent system overview. • [Profile Module] To expand the applicability of 4KAgent, we propose a Profile Module to bring the availability to customize the system for different restoration tasks. 4KAgent can adapt to different restoration tasks without extra training. • [DIV4K-50 Dataset] To evaluate 4K super-resolution performances, we build the DIV4K-50 dataset as a challenging testset to upscale a low-quality (LQ)… view at source ↗

**Figure 3.** Face restoration pipeline overview. The overall framework of the face restore pipeline in the 4KAgent system is shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (20 more)

**Figure 4.** Figure 4: Visual comparisons on classical image SR task. (Please zoom in to see details.) [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

**Figure 5.** Figure 5: Visual comparisons on real-world image SR task. (Please zoom in to see details) [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

**Figure 6.** Figure 6: Visual comparisons on multiple-degradation IR task. (Please zoom in to see details) [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Visual comparisons on face restoration task. (Please zoom in to see details) [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]

**Figure 8.** Figure 8: Visual comparisons on RealSRSet dataset (16 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]

**Figure 9.** Figure 9: Visual comparisons on DIV4K-50 dataset. (Please zoom in to see details) [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]

**Figure 10.** Figure 10: Visual comparison between native 4K image generation and 1K image generation methods [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

**Figure 11.** Figure 11: Visual comparison of aesthetic preference alignment between SANA-4K and SANA [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]

**Figure 12.** Figure 12: Visual comparison on AID dataset (160→640) [PITH_FULL_IMAGE:figures/full_fig_p028_12.png]

**Figure 13.** Figure 13: Visual comparison on DIOR dataset (128→512). 28 [PITH_FULL_IMAGE:figures/full_fig_p028_13.png]

**Figure 14.** Figure 14: Visual comparison on DOTA dataset (128→512) [PITH_FULL_IMAGE:figures/full_fig_p029_14.png]

**Figure 15.** Figure 15: Visual comparison on WorldStrat dataset (160 [PITH_FULL_IMAGE:figures/full_fig_p029_15.png]

**Figure 16.** Figure 16: Visual comparison on DOTA dataset (4K upscaling). [PITH_FULL_IMAGE:figures/full_fig_p030_16.png]

**Figure 17.** Figure 17: Visualization of fluorescence microscopy image SR on SR-CACO-2 dataset (64 [PITH_FULL_IMAGE:figures/full_fig_p033_17.png]

**Figure 18.** Figure 18: Visual comparison of pathology image super-resolution on bcSR dataset (256 [PITH_FULL_IMAGE:figures/full_fig_p035_18.png]

**Figure 19.** Figure 19: Visual comparison of X-Ray image SR on Chest X-ray 2017 and Chest X-ray 14 dataset. [PITH_FULL_IMAGE:figures/full_fig_p037_19.png]

**Figure 20.** Figure 20: Visual comparison of Ultrasound image SR on US-CASE and MMUS1K dataset. [PITH_FULL_IMAGE:figures/full_fig_p038_20.png]

**Figure 21.** Figure 21: Visual comparison of fundoscopy image SR on DRIVE dataset (128 [PITH_FULL_IMAGE:figures/full_fig_p038_21.png]

**Figure 22.** Figure 22: Visual comparisons for ablation study on Q-MoE. [PITH_FULL_IMAGE:figures/full_fig_p039_22.png]

**Figure 23.** Figure 23: Visual comparisons for ablation study on face restoration pipeline. [PITH_FULL_IMAGE:figures/full_fig_p039_23.png]

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Forward citations

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